Abstract In this proposal, we will perform integrative analysis for multi-omics data generated in the Trans-Omics for Precision Medicine (TOPMed) study to identify the molecular mechanism driving multiple blood lipid traits and coronary heart disease (CHD). Blood lipids are highly heritable and modifiable biomarkers for CHD, and therapeutic modification of blood lipid levels is an effective strategy for reducing CHD risk. TOPMed is expected to generate >100,000 sequenced whole genomes, and epigenomics, transcriptomics, metabolomics, and proteomics data on >10,000 of these individuals. These rich datasets will provide an outstanding opportunity to better understand underlying biology and provide novel molecular targets. We will integratively analyze multi-omics data to 1) identify genetic variants, methylation sites and genes that are associated with lipid and CHD phenotypes, and evaluate their total contribution to the phenotypic variation as well as their ability to predict phenotypes; 2) identify functional variants associated with methylation and gene expression/translation levels, and incorporate such functional information to improve the power of genome- wide association mapping; and 3) identify the causal networks from genetic variations to phenotypic variations, and incorporate the network structure to improve power for identifying novel genes associated with the phenotypes. Successful completion of these aims will provide new insights into molecular mechanisms and biomarkers that can be translated into the prevention and treatment of CHD.